






'''simple docstring'''







import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
UpperCamelCase     :   int	      =		[
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]
def     A__      (					__lowerCAmelCase		:      Union[str, Any]    ):
						# Test all the extensions added in the setup
						for file in FILES_TO_FIND:
												if not (transformers_path / file).exists():
																		return False
						return True


if __name__ == "__main__":
							UpperCamelCase     :   Dict	      =		argparse.ArgumentParser()
							parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
							UpperCamelCase     :   Union[str, Any]	      =		parser.parse_args()
							if args.check_lib:
														UpperCamelCase     :   Optional[int]	      =		importlib.import_module('transformers')
														UpperCamelCase     :   int	      =		Path(transformers_module.__file__).parent
							else:
														UpperCamelCase     :   List[str]	      =		Path.cwd() / 'build/lib/transformers'
							if not test_custom_files_are_present(transformers_path):
														raise ValueError('The built release does not contain the custom files. Fix this before going further!')






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a      :  Union[str, Any]			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	CLIPConfig

			A      		=	['''CLIPEncoderLayer''']

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						super().__init__(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     CLIPVisionModelWithProjection(config.vision_config					)

						UpperCAmelCase_:		Tuple							      =     nn.Linear(config.vision_config.projection_dim,   1					)
						UpperCAmelCase_:		Tuple							      =     nn.Linear(config.vision_config.projection_dim,   1					)

			@torch.no_grad()
			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=0.5,   SCREAMING_SNAKE_CASE_=0.5					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     self.vision_model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		List[str]							      =     self.p_head(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     nsfw_detected.flatten()
						UpperCAmelCase_:		List[Any]							      =     nsfw_detected > p_threshold
						UpperCAmelCase_:		Any							      =     nsfw_detected.tolist()

						if any(SCREAMING_SNAKE_CASE_					):
									logger.warning(
									    """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
									    """ Try again with a different prompt and/or seed."""					)

						for idx, nsfw_detected_ in enumerate(SCREAMING_SNAKE_CASE_					):
									if nsfw_detected_:
												UpperCAmelCase_:		Tuple							      =     np.zeros(images[idx].shape					)

						UpperCAmelCase_:		str							      =     self.w_head(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     watermark_detected.flatten()
						UpperCAmelCase_:		Tuple							      =     watermark_detected > w_threshold
						UpperCAmelCase_:		int							      =     watermark_detected.tolist()

						if any(SCREAMING_SNAKE_CASE_					):
									logger.warning(
									    """Potential watermarked content was detected in one or more images. A black image will be returned instead."""
									    """ Try again with a different prompt and/or seed."""					)

						for idx, watermark_detected_ in enumerate(SCREAMING_SNAKE_CASE_					):
									if watermark_detected_:
												UpperCAmelCase_:		Any							      =     np.zeros(images[idx].shape					)

						return images, nsfw_detected, watermark_detected






